Two stage instance segmentation methods [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]} achieve state-of-the-art performance by first detecting bounding boxes and then performing segmentation in each ROI region.
FCIS [1]} introduces the position-sensitive score maps within instance proposals for mask segmentation.
Mask R-CNN [2]} extends Faster R-CNN [10]} with a FCN branch to segment objects in the detected box.
PANet [3]} further integrates multi-level feature of FPN to enhance feature representation.
MS R-CNN [12]} mitigates the misalignment between mask quality and score.
CenterMask [13]} is built upon the anchor free detector FCOS [14]} with a SAG-Mask branch.
In contrast, our BCNet is a bilayer mask prediction network for addressing the issues of heavy occlusion and overlapping objects in two-stage instance segmentation. Experiments validate that our approach leads to significant performance gain on overall instance segmentation performance not limited to heavily occluded cases.
